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benchmark-functions

Benchmark Python functions with warm-up iterations and statistical analysis.

18|5|Updated Nov 3, 2025
Search Tags:#performance#python#optimization#benchmarking#metrics#profiling#mojo
One-click install
npx skills add https://github.com/mvillmow/ProjectOdyssey --skill benchmark-functions
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-functions
Source: https://github.com/mvillmow/ProjectOdyssey/tree/main/.claude/skills/tier-2/benchmark-functions
Command: npx skills add https://github.com/mvillmow/ProjectOdyssey --skill benchmark-functions

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, mojo.

What problem does it solve?

Identifying performance bottlenecks and comparing different algorithm implementations requires systematic and accurate measurement, which can be complex to set up manually.

Core Features & Use Cases

  • Systematic Measurement: Measures function execution time, memory usage, and performance characteristics with warm-up iterations and statistical analysis.
  • Implementation Comparison: Allows direct comparison of different algorithm implementations or performance before/after optimization.
  • Use Case: When evaluating two different implementations of a matrix multiplication algorithm, use this skill to benchmark both, providing mean, median, and standard deviation of execution times to determine the faster approach.

Quick Start

Use the benchmark-functions skill to benchmark a Python function 'module.function(args)' for 1000 iterations.

Frequently Asked Questions about benchmark-functions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark Python functions to measure performance?▼

Benchmarking Python functions involves running them repeatedly with warm-up iterations, collecting execution-time statistics (mean, median, standard deviation), and comparing results across implementations. This skill automates that measurement process to identify which algorithm or optimization approach performs best.

Can I use benchmarking to compare SIMD versus scalar implementations?▼

Yes. Benchmarking systematically measures execution time for both SIMD and scalar implementations side-by-side, providing statistical comparison to quantify performance differences and determine which approach is faster for your use case.

What's the best way to establish performance baselines for CI/CD pipelines?▼

Use benchmarking to measure and record baseline performance metrics during development, then compare future runs against those baselines in CI/CD workflows. This detects performance regressions early and tracks optimization impact over time.

How do I identify performance bottlenecks in my code?▼

Benchmarking with statistical analysis reveals which functions or algorithms consume the most execution time. By measuring mean, median, and standard deviation across repeated runs, you pinpoint where optimization efforts will have the greatest impact.

Can benchmarking track memory usage alongside execution time?▼

Yes. This skill includes optional memory usage tracking alongside timing measurements, allowing you to evaluate both speed and memory efficiency when comparing different implementations.

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